Genetic Epidemiology最新文献

{"title":"Uncertainty Quantification in Epigenetic Clocks via Conformalized Quantile Regression","authors":"Yanping Li,&nbsp;Jaclyn M. Goodrich,&nbsp;Karen E. Peterson,&nbsp;Peter X.-K. Song,&nbsp;Lan Luo","doi":"10.1002/gepi.70008","DOIUrl":"https://doi.org/10.1002/gepi.70008","url":null,"abstract":"<p>DNA methylation (DNAm) is a chemical modification of DNA that can be influenced by various factors, including age, the environment, and lifestyle. An epigenetic clock is a predictive tool that measures biological age based on DNAm levels. It can provide insights into an individual's biological age, which may differ from their chronological age. This difference, known as the epigenetic age acceleration, may reflect health status and the risk for age-related diseases. Moreover, epigenetic clocks are used in studies of aging to assess the effectiveness of antiaging interventions and to understand the underlying mechanisms of aging and disease. Various epigenetic clocks have been developed using samples from different populations, tissues, and cell types, typically by training high-dimensional linear regression models with an elastic net penalty. While these models can predict mean biological age based on DNAm with high precision, there is a lack of uncertainty quantification which is important for interpreting the precision of age estimations and for clinical decision-making. To understand the distribution of a biological age clock beyond its mean, we propose a general pipeline for training epigenetic clocks, based on an integration of high-dimensional quantile regression and conformal prediction, to effectively reveal population heterogeneity and construct prediction intervals. Our approach produces adaptive prediction intervals not only achieving nominal coverage but also accounting for the inherent variability across individuals. By using the data collected from 728 blood samples in 11 DNAm data sets from children, we find that our quantile regression-based prediction intervals are narrower than those derived from conventional mean regression-based epigenetic clocks. This observation demonstrates an improved statistical efficiency over the existing pipeline for training epigenetic clocks. In addition, the resulting intervals have a synchronized varying pattern to age acceleration, effectively revealing cellular evolutionary heterogeneity in age patterns in different developmental stages during individual childhoods and adolescent cohort. Our findings suggest that conformalized high-dimensional quantile regression can produce valid prediction intervals and uncover underlying population heterogeneity. Although our methodology focuses on the distribution of measures of biological aging in children, it is applicable to a broader range of age groups to improve understanding of epigenetic age beyond the mean. This inference-based toolbox could provide valuable insights for future applications of epigenetic interventions for age-related diseases.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parent-of-Origin Effects in Childhood Asthma at Seven Years of Age","authors":"Yunsung Lee,&nbsp;Miriam Gjerdevik,&nbsp;Astanand Jugessur,&nbsp;Håkon Kristian Gjessing,&nbsp;Elizabeth Corfield,&nbsp;Alexandra Havdahl,&nbsp;Jennifer Ruth Harris,&nbsp;Maria Christine Magnus,&nbsp;Siri Eldevik Håberg,&nbsp;Per Magnus","doi":"10.1002/gepi.70007","DOIUrl":"https://doi.org/10.1002/gepi.70007","url":null,"abstract":"<p>Childhood asthma is more common among children whose mothers have asthma than among those whose fathers have asthma. The reasons for this are unknown, and we hypothesize that genomic imprinting may partly explain this observation. Our aim is to assess parent-of-origin (PoO) effects on childhood asthma by analyzing SNP array genotype data from a large population-based cohort. To estimate PoO effects in parent-reported childhood asthma at 7 years of age, we fit a log-linear model implemented in the HAPLIN R package to SNP array genotype data from 915 mother–father–child case triads, 603 mother–child case dyads, and 113 father–child case dyads participating in the Norwegian Mother, Father, and Child Cohort Study (MoBa). We found that alleles at two SNPs—rs3003214 and rs3003211—near the adenylosuccinate synthase 2 gene (<i>ADSS2</i> on chromosome 1q44) showed significant PoO effects at a false positive rate ≤ 0.05. The ratio of the effect of the maternally and paternally inherited G-allele at rs3003214 was 1.68 (95% CI: 1.41–2.03, <i>p</i> value = 1.13E−08). Our results suggest PoO effects at the <i>ADSS2</i> gene, particularly the maternally inherited G-allele at rs3003214, may contribute to the maternal effect in childhood asthma.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dimension Reduction Using Local Principal Components for Regression-Based Multi-SNP Analysis in 1000 Genomes and the Canadian Longitudinal Study on Aging (CLSA)","authors":"Fatemeh Yavartanoo,&nbsp;Myriam Brossard,&nbsp;Shelley B. Bull,&nbsp;Andrew D. Paterson,&nbsp;Yun Joo Yoo","doi":"10.1002/gepi.70005","DOIUrl":"https://doi.org/10.1002/gepi.70005","url":null,"abstract":"<div>\u0000 \u0000 <p>For genetic association analysis based on multiple SNP regression of genotypes obtained by dense DNA sequencing or array data imputation, multi-collinearity can be a severe issue causing failure to fit the regression model. In this study, we propose a method of Dimension Reduction using Local Principal Components (DRLPC) which aims to resolve multi-collinearity by removing SNPs under the assumption that the remaining SNPs can capture the effect of a removed SNP due to high linear dependency. This approach to dimension reduction is expected to improve the power of regression-based statistical tests. We apply DRLPC to chromosome 22 SNPs of two data sets, the 1000 Genomes Project (phase 3) and the Canadian Longitudinal Study on Aging (CLSA), and calculate variance inflation factors (VIF) in various SNP-sets before and after implementing DRLPC as a metric of collinearity. Notably, DRLPC addresses multi-collinearity by excluding variables with a VIF exceeding a predetermined threshold (VIF = 20), thereby improving applicability for subsequent regression analyses. The number of variables in a final set for regression analysis is reduced to around 20% on average for larger-sized genes, whereas for smaller ones, the proportion is around 48%; suggesting that DRLPC is particularly effective for larger genes. We also compare the power of several multi-SNP statistics constructed for gene-specific analysis to evaluate power gains achieved by DRLPC. In simulation studies based on 100 genes with ≤ 500 SNPs per gene, DRLPC increases the power of the multiple regression Wald test from 60% to around 80%.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex-Specific Association Between Polymorphisms in Estrogen Receptor Alpha Gene (ESR1) and Depression: A Genome-Wide Association Study of All of Us and UK Biobank Data","authors":"Yue Hu,&nbsp;Menglu Che,&nbsp;Heping Zhang","doi":"10.1002/gepi.70004","DOIUrl":"https://doi.org/10.1002/gepi.70004","url":null,"abstract":"<div>\u0000 \u0000 <p>Major depressive disorder (MDD) is prevalent worldwide, substantially and negatively impacting both the quality and length of life of 280 million people globally. The genetic risk factors of MDD have been studied in various previous research, but the findings lack consistency. Sex/gender and racial/ethnic disparities have been reported; however, many previous genetic studies, represented by large-scale genome-wide association studies (GWASs) are known to lack diversity in the study cohorts. All of Us is a biorepository aiming to focus on the historically underrepresented groups. We perform GWASs for the MDD phenotype, using over 200,000 participants' genotypes and carry out sex- and racial/ethnic-specific subgroup studies. We identified a risk locus (chr6:151945242) in Estrogen Receptor Alpha Gene (<i>ESR1</i>) (<i>p</i> = <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mn>1.70</mn>\u0000 \u0000 <mo>×</mo>\u0000 \u0000 <msup>\u0000 <mn>10</mn>\u0000 \u0000 <mrow>\u0000 <mo>−</mo>\u0000 \u0000 <mn>9</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> $1.70times {10}^{-9}$</annotation>\u0000 </semantics></math>), and further confirmed the genetic association is sex-specific. The single-nucleotide polymorphism (SNP) chr6:151945242 was significant only in the male group, but not in the female group. These findings were replicated in the UK Biobank and echo with existing studies on the <i>ESR1</i> gene and depressive disorders. Our results indicate that the All of Us program is a reliable resource for GWAS, as well as shedding light on further investigation of sex- and racial/ethnic-specific genome association, especially in underrepresented groups of the US population.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reference-Based Standardization Approach Stabilizing Small Batch Risk Prediction via Polygenic Score","authors":"Yoichi Sutoh,&nbsp;Tsuyoshi Hachiya,&nbsp;Yayoi Otsuka-Yamasaki,&nbsp;Tomoharu Tokutomi,&nbsp;Akiko Yoshida,&nbsp;Yuka Kotozaki,&nbsp;Shohei Komaki,&nbsp;Shiori Minabe,&nbsp;Hideki Ohmomo,&nbsp;Kozo Tanno,&nbsp;Akimune Fukushima,&nbsp;Makoto Sasaki,&nbsp;Atsushi Shimizu","doi":"10.1002/gepi.70002","DOIUrl":"10.1002/gepi.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>The polygenic score (PGS) holds promise for motivating preventive behavioral changes. However, no clinically validated standardization methodology currently exists. Here, we demonstrate the efficacy of a “reference-based” approach for standardization. This method uses the PGS distribution in the general population as a reference for normalization and percentile determination; however, it has not been validated. We investigated three potential influences on PGS computation: (1) the size of the reference population, (2) biases associated with different genotyping platforms, and (3) inclusion of kinship ties within the reference group. Our results indicate that the reference size affects the bootstrap estimate of standard error for PGS percentiles, peaking around the 50th percentile and diminishing at extreme percentiles (1st or 100th). Discrepancies between genotyping platforms, such as different microarrays and whole-genome sequencing, resulted in deviations in PGS (<i>p</i> &lt; 0.05 in Kolmogorov–Smirnov test). However, these deviations were reduced to a nonsignificant level using shared genetic variants in the calculations when the ancestry of the samples and reference were matched. This approach recovered approximately 9.6% of the positive predictive value of PGS by naïve genotype. Our results provide fundamental insights for establishing clinical guidelines for implementing PGS to communicate reliable risks to individuals.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143065247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RetroFun-RVS: A Retrospective Family-Based Framework for Rare Variant Analysis Incorporating Functional Annotations","authors":"Loïc Mangnier,&nbsp;Ingo Ruczinski,&nbsp;Jasmin Ricard,&nbsp;Claudia Moreau,&nbsp;Simon Girard,&nbsp;Michel Maziade,&nbsp;Alexandre Bureau","doi":"10.1002/gepi.70001","DOIUrl":"10.1002/gepi.70001","url":null,"abstract":"<p>A large proportion of genetic variations involved in complex diseases are rare and located within noncoding regions, making the interpretation of underlying biological mechanisms a daunting task. Although technical and methodological progress has been made to annotate the genome, current disease-rare-variant association tests incorporating such annotations suffer from two major limitations. First, they are generally restricted to case−control designs of unrelated individuals, which often require tens or hundreds of thousands of individuals to achieve sufficient power. Second, they were not evaluated with region-based annotations needed to interpret the causal regulatory mechanisms. In this work, we propose RetroFun-RVS, a new retrospective family-based score test, incorporating functional annotations. A critical feature of the proposed method is to aggregate genotypes to compare against rare variant-sharing expectations among affected family members. Through extensive simulations, we have demonstrated that RetroFun-RVS integrating networks based on 3D genome contacts as functional annotations reach greater power over the region-wide test, other strategies to include subregions and competing methods. Also, the proposed framework shows robustness to non-informative annotations, maintaining its power when causal variants are spread across regions. Asymptotic <i>p</i>-values are susceptible to Type I error inflation when the number of families with rare variants is small, and a bootstrap procedure is recommended in these instances. Application of RetroFun-RVS is illustrated on whole genome sequence in the Eastern Quebec Schizophrenia and Bipolar Disorder Kindred Study with networks constructed from 3D contacts and epigenetic data on neurons. In summary, the integration of functional annotations corresponding to regions or networks with transcriptional impacts in rare variant tests appears promising to highlight regulatory mechanisms involved in complex diseases.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11775437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene−Air Pollution Interaction and Diversity of Genetic Sampling: The Southern California Children's Health Study","authors":"Justine Po,&nbsp;John Morrison,&nbsp;Brittney Marian,&nbsp;Zhanghua Chen,&nbsp;W. James Gauderman,&nbsp;Erika Garcia","doi":"10.1002/gepi.70000","DOIUrl":"10.1002/gepi.70000","url":null,"abstract":"<div>\u0000 \u0000 <p>Gene−environment interactions have been observed for childhood asthma, however few have been assessed in ethnically diverse populations. Thus, we examined how polygenic risk score (PRS) modifies the association between ambient air pollution exposure (nitrogen dioxide [NO₂], ozone, particulate matter &lt; 2.5 and &lt; 10 μm) and childhood asthma incidence in a diverse cohort. Participants (<i>n</i> = 1794) were drawn from the Southern California Children's Health Study, a multi-wave prospective cohort followed from 4th to 12th grade. PRS was developed using single nucleotide polymorphisms previously associated with childhood asthma. PRS−asthma associations and PRS−air pollutant interactions were estimated using Poisson regression. An interquartile range PRS increase was associated with 36% (95% CI: 9%, 70%) higher asthma incidence among non-Hispanic children, but not associated with asthma among Hispanic children (rate ratio: 0.81 [95% CI: 0.62, 1.04]). NO₂−PRS interaction was borderline significant in the overall sample (coefficient: 0.23 [95% CI: −0.03, 0.49]). Limited evidence was observed for a positive interaction between PRS and NO₂ exposure associated with asthma incidence; however, the literature-based PRS was not associated with asthma among Hispanic participants. Equitable, diverse genetic sampling approaches are needed to better identify clinically relevant SNPs in this population.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Statistical Method for Unmasking Sex-Specific Genomics Signatures in Complex Traits","authors":"Samaneh Mansouri,&nbsp;Mélissa Rochette,&nbsp;Benoit Labonté,&nbsp;Qingrun Zhang,&nbsp;Ting-Huei Chen","doi":"10.1002/gepi.22612","DOIUrl":"10.1002/gepi.22612","url":null,"abstract":"<div>\u0000 \u0000 <p>Genotype–phenotype association studies have advanced our understanding of complex traits but often overlook sex-specific genetic signals. The growing awareness of sex-specific influences on human traits and diseases necessitates tailored statistical methodologies to dissect these genetic intricacies. Rare genetic variants play a significant role in disease development, often exhibiting stronger per-allele effects than common variants. In sex-dimorphic analysis, traits are viewed as having two sex-specific subsets rather than being uniformly defined. Existing methods for gene-based analysis of rare variants across multiple traits can identify shared genetic signals but cannot reveal the specific subsets from which significant signals originate. This means that when a significant signal is detected, it remains unclear whether it arises from the male samples, female samples, or both. To address this limitation, we propose <i>SubsetRV</i>, a new methodology capable of identifying genes associated with specific traits or diseases in males, females, or both. <i>SubsetRV</i> can also be applied to broader applications in multiple traits analysis. Simulation studies have demonstrated <i>SubsetRV</i>'s reliability, and real data analysis on bipolar disorder and schizophrenia has revealed potential sex-specific genetic signals. <i>SubsetRV</i> offers a valuable tool for identifying sex-specific genetic candidates, aiding in understanding disease mechanisms. An R package for <i>SubsetRV</i> is available on GitHub. It can be accessed directly through this link: https://github.com/Mansouri-S/SubsetRV.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Disease Associated Multi-Omics Network With Mixed Graphical Models Based on Markov Random Field Model","authors":"Jaehyun Park,&nbsp;Sungho Won","doi":"10.1002/gepi.22605","DOIUrl":"10.1002/gepi.22605","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, we proposed a new method named fused mixed graphical model (FMGM), which can infer network structures associated with dichotomous phenotypes. FMGM is based on a pairwise Markov random field model, and statistical analyses including the proposed method were conducted to find biological markers and underlying network structures of the atopic dermatitis (AD) from multiomics data of 6-month-old infants. The performance of FMGM was evaluated with simulations by using synthetic datasets of power-law networks, showing that FMGM had superior performance for identifying the differences of the networks compared to the separate inference with the previous method, causalMGM (F1-scores 0.550 vs. 0.730). Furthermore, FMGM was applied to identify multiomics profiles associated with AD, and significance association was found for the correlation between carotenoid biosynthesis and RNA degradation, suggesting the importance of metabolism related to oxidative stress and microbial RNA balance. R codes can be accessed as an R package “fusedMGM,” and an example data set and a script for analyses can be found at http://figshare.com/articles/dataset/FMGM_synthetic_data_example_zip/20509113.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetically Predicted Gene Expression Effects on Changes in Red Blood Cell and Plasma Polyunsaturated Fatty Acids","authors":"Nikhil K. Khankari,&nbsp;Timothy Su,&nbsp;Qiuyin Cai,&nbsp;Lili Liu,&nbsp;Elizabeth A. Jasper,&nbsp;Jacklyn N. Hellwege,&nbsp;Harvey J. Murff,&nbsp;Martha J. Shrubsole,&nbsp;Jirong Long,&nbsp;Todd L. Edwards,&nbsp;Wei Zheng","doi":"10.1002/gepi.22613","DOIUrl":"10.1002/gepi.22613","url":null,"abstract":"<p>Polyunsaturated fatty acids (PUFAs) including omega-3 and omega-6 are obtained from diet and can be measured objectively in plasma or red blood cells (RBCs) membrane biomarkers, representing different dietary exposure windows. In vivo conversion of omega-3 and omega-6 PUFAs from short- to long-chain counterparts occurs via a shared metabolic pathway involving fatty acid desaturases and elongase. This analysis leveraged genome-wide association study (GWAS) summary statistics for RBC and plasma PUFAs, along with expression quantitative trait loci (eQTL) to estimate tissue-specific genetically predicted gene expression effects for delta-5 desaturase (<i>FADS1</i>), delta-6 desaturase (<i>FADS2</i>), and elongase (<i>ELOVL2</i>) on changes in RBC and plasma biomarkers. Using colocalization, we identified shared variants associated with both increased gene expression and changes in RBC PUFA levels in relevant PUFA metabolism tissues (i.e., adipose, liver, muscle, and whole blood). We observed differences in RBC versus plasma PUFA levels for genetically predicted increase in <i>FADS1</i> and <i>FADS2</i> gene expression, primarily for omega-6 PUFAs linoleic acid (LA) and arachidonic acid (AA). The colocalization analysis identified rs102275 to be significantly associated with a 0.69% increase in total RBC membrane-bound LA levels (<i>p</i> = 5.4 × 10⁻¹²). Future PUFA genetic studies examining long-term PUFA biomarkers are needed to confirm our results.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11734643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}